Computational intelligence and neuroscience
Aug 21, 2022
It is hard to forecasting oil future prices accurately, which is affected by some nonlinear, nonstationary, and other chaotic characteristics. Then, a novel GA-SVR-GRNN hybrid deep learning algorithm is put forward for forecasting oil future price. F...
In materials science, machine learning has been intensively researched and used in various applications. However, it is still far from achieving intelligence comparable to that of human experts in terms of creativity and explainability. In this paper...
Industry 4.0 lets the industry build compact, precise, and connected assets and also has made modern industrial assets a massive source of data that can be used in process optimization, defining product quality, and predictive maintenance (PM). Large...
Animal societies exhibit complex dynamics that require multi-level descriptions. They are difficult to model, as they encompass information at different levels of description, such as individual physiology, individual behaviour, group behaviour and f...
In this article, a new path planning algorithm is proposed. The algorithm is developed on the basis of the algorithm for finding the best value using multi-objective evolutionary particle swarm optimization, known as the MOEPSO. The proposed algorith...
Neural networks : the official journal of the International Neural Network Society
Aug 18, 2022
In this paper, a synergetic learning structure-based neuro-optimal fault tolerant control (SLSNOFTC) method is proposed for unknown nonlinear continuous-time systems with actuator failures. Under the framework of the synergetic learning structure (SL...
A large number of experiments have proved that the ring structure is a common phenomenon in neural networks. Nevertheless, a few works have been devoted to studying the neurodynamics of networks with only one ring. Little is known about the dynamics ...
In this article, a novel interval type-2 Takagi-Sugeno fuzzy c -regression modeling method with a modified distance definition is proposed. The modified distance definition is developed to describe the distance between each data point and the local t...
This article investigates the reinforcement-learning (RL)-based disturbance rejection control for uncertain nonlinear systems having nonsimple nominal models. An extended state observer (ESO) is first designed to estimate the system state and the tot...
This article addresses the finite-time event-triggered adaptive neural control for fractional-order nonlinear systems. Based on the backstepping technique, a novel adaptive event-triggered control scheme is proposed, and finite-time stability criteri...